Automatic segmentation of 4D cardiac MR images for extraction of ventricular chambers using a spatio-temporal approach

Angélica Atehortúa; Maria A. Zuluaga; Sébastien Ourselin; Diana Giraldo; Eduardo Romero

doi:10.1117/12.2217076

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21 March 2016 Automatic segmentation of 4D cardiac MR images for extraction of ventricular chambers using a spatio-temporal approach

Angélica Atehortúa , Maria A. Zuluaga, Sébastien Ourselin, Diana Giraldo, Eduardo Romero

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Proceedings Volume 9784, Medical Imaging 2016: Image Processing; 978435 (2016) https://doi.org/10.1117/12.2217076
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

An accurate ventricular function quantification is important to support evaluation, diagnosis and prognosis of several cardiac pathologies. However, expert heart delineation, specifically for the right ventricle, is a time consuming task with high inter-and-intra observer variability. A fully automatic 3D+time heart segmentation framework is herein proposed for short-axis-cardiac MRI sequences. This approach estimates the heart using exclusively information from the sequence itself without tuning any parameters. The proposed framework uses a coarse-to-fine approach, which starts by localizing the heart via spatio-temporal analysis, followed by a segmentation of the basal heart that is then propagated to the apex by using a non-rigid-registration strategy. The obtained volume is then refined by estimating the ventricular muscle by locally searching a prior endocardium- pericardium intensity pattern. The proposed framework was applied to 48 patients datasets supplied by the organizers of the MICCAI 2012 Right Ventricle segmentation challenge. Results show the robustness, efficiency and competitiveness of the proposed method both in terms of accuracy and computational load.

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Angélica Atehortúa , Maria A. Zuluaga, Sébastien Ourselin, Diana Giraldo, and Eduardo Romero "Automatic segmentation of 4D cardiac MR images for extraction of ventricular chambers using a spatio-temporal approach", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 978435 (21 March 2016); https://doi.org/10.1117/12.2217076

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